Mapping and correlating
crime/ASB and Private Sector Housing Using the Gi* Statistic

Gi* (Getis-Ord statistic)
analysis was conducted to inform decision-making in relation to whether and
where to apply a scheme for licensing Private Rental Sector (PRS) landlords in
the London Borough of Newham. A Local Authority can implement landlord
licensing where PRS housing is found to be contributing to crime and disorder,
in order to better control the behaviour of tenants by regulating landlords. An
evidence package was compiled in support of borough-wide PRS licensing,
including specific evidence of PRS housing causing crime/ASB and evidence of a
correlation between the locations of PRS housing and hotspots for relevant
types of crime and ASB. This paper presents the latter analysis, which
determined where in Newham PRS and relevant crime/ASB co-occur, i.e. where
there are overlapping concentrations of both PRS and crime/ASB, and whether
these concentrations were statistically correlated.

Data included ASB records
from the Council’s UNI-form database received 1/4/11-31/3/12 relating to:

Police CRIS ‘geocoded’ crime
records with ‘minor code’ Burglary-Dwelling
and Police Computer Aided Dispatch (CAD) records (999 and other calls to
the police) for Home Office defined ASB codes plus code 11 Drug Offences
received 1/4/11-31/3/12 were also analysed. PRS housing locations were provided
as a map point layer by the Council’s Geographical Information Systems Officer.

The Getis-Ord Gi* statistic
was used to calculate the concentrations for each dataset, based on counts of
incidents within grid cells. Cells with statistically significant Gi* z-scores
at the generally accepted probability level of p<.05 (i.e. high
concentrations or ‘hotspots’) were displayed on maps colour coded. Overlaying
the hotspot maps for the PRS (blue) and other datasets (pink) then revealed the
locations where there were significantly higher than average levels both PRS
and ASB (purple). To determine whether there was a significant correlation
between these concentrations of PRS and crime/ASB, correlation analysis was
conducted comparing both the number of PRS properties per cell and its PRS Gi*
z-score with the number of crime/ASB records per cell and its GI* z-score.

PRS was statistically
significantly correlated with all datasets, meaning the more PRS in a location,
the more crime/ASB (of any given type) there is and conversely that locations
with fewer PRS properties also tend to see less of the crime/ASB types
considered. Each crime/ASB type tended to show small hotspots scattered
throughout the borough, varying by crime/ASB type. But cumulatively, overlaying
all the crime/ASB types showed a much wider spread overlap between crime/ASB
overall and PRS. This provided support for implementing PRS licensing on a
borough-wide scale. It was emphasised in the resulting report that correlation
does not imply causation – where PRS and crime/ASB were correlated, this does
not mean that privately rented accommodation is a cause of ASB, only that they
tend to occur in the same locations. Based on this analysis plus other, causal
evidence, borough-wide PRS licensing was subsequently implemented.

Estimating offence times
from victim reports

Matt Ashby and Kate Bowers, Department of Security and
Crime Science, University College London

Crime analysts often attempt
to draw conclusions about the temporal distribution of crime from reports of
previous offences. This task is complicated in the case of many crimes against
unattended property because victims of crime cannot tell police when the offence
occurred.

This study used CCTV records
to compare several previously-proposed methods for estimating offence times
from victims’ reporting of when they left their property unattended and when
they discovered the crime had occurred. These methods are all in widespread use
by crime analysts in the UK and elsewhere, but they have not previously been
tested because of a lack of suitable data.

This study found that two
commonly-used methods, which are based on unsupportable assumptions about
offender behaviour, to be inaccurate and misleading. It also found another
method (aoristic analysis) to be accurate in the cases studied. This led to two
conclusions: that the inaccurate methods could lead analysts astray (and so
should not be used) and that aoristic analysis appears to be a suitable
solution to the problem of temporal inaccuracy in this case.

The results of this study
are being used by those training crime analysts and by analysts in British
Transport Police to promote the use of aoristic analysis and to warn
practitioners about the potential inaccuracies of some other commonly-used
methods.